Applied BioMath (www.appliedbiomath.com), the industry-leader in providing model-informed drug discovery and development (MID3) support to help accelerate and de-risk therapeutic research and development (R&D), today announced their participation at the Bio-IT World Conference and Expo occurring May 3-5, 2022 in Boston, MA.
Kas Subramanian, PhD, Executive Director of Modeling at Applied BioMath will present "Applications of Machine Learning in Preclinical Drug Discovery" within the conference track, AI for Drug Discovery and Development on Thursday, May 5, 2022 at 1:05 p.m. E.T. In this presentation, Dr. Subramanian will discuss how machine learning methods can improve efficiency in therapeutic R&D decision making. He will review case studies that demonstrate machine learning applications to target validation and lead optimization.
"Traditionally, therapeutic R&D requires experiments on many different targets, hits, leads, and candidates that are based on best guesses," said John Burke, PhD, Co-founder, President and CEO of Applied BioMath. "By utilizing artificial intelligence and machine learning, project teams can computationally work with more data to better inform experiments and develop better therapeutics."
To learn more about Applied BioMath's presence at the Bio-IT World Conference and Expo, please visit www.appliedbiomath.com/BioIT22.
About Applied BioMath
Founded in 2013, Applied BioMath's mission is to revolutionize drug invention. Applied BioMath applies biosimulation, including quantitative systems pharmacology, PKPD, bioinformatics, machine learning, clinical pharmacology, and software solutions to provide quantitative and predictive guidance to biotechnology and pharmaceutical companies to help accelerate and de-risk therapeutic research and development. Their approach employs proprietary algorithms and software to support groups worldwide in decision-making from early research through all phases of clinical trials. The Applied BioMath team leverages their decades of expertise in biology, mathematical modeling and analysis, high-performance computing, and industry experience to help groups better understand their therapeutic, its best-in-class parameters, competitive advantages, patients, and the best path forward into and in the clinic to increase likelihood of clinical concept and proof of mechanism, and decrease late stage attrition rates. For more information about Applied BioMath and its services and software, visit www.appliedbiomath.com.
Applied BioMath and the Applied BioMath logo are registered trademarks of Applied BioMath, LLC.
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